msAggr_sctransform-v1
Creating new pipeline using seurat v4.0.2 available 2021.06.08
Load libraries required for Seuratv4
knitr::opts_knit$set(root.dir = "~/Desktop/10XGenomicsData/msAggr_scRNASeq/")
library(dplyr)
Attaching package: ‘dplyr’
The following objects are masked from ‘package:stats’:
filter, lag
The following objects are masked from ‘package:base’:
intersect, setdiff, setequal, unionlibrary(Seurat)Registered S3 method overwritten by 'data.table':
method from
print.data.table
Registered S3 method overwritten by 'htmlwidgets':
method from
print.htmlwidget tools:rstudio
Attaching SeuratObjectlibrary(patchwork)
library(ggplot2)
# library(clustree)store session info
sink("msAggr_sct-v1.20210615")
sessionInfo()
sink()A note about using SCTransform versus ScaleData
https://bioconductor.org/packages/3.10/workflows/vignettes/simpleSingleCell/inst/doc/batch.html#62_for_gene-based_analyses >You can also normalize and scale data for the RNA assay. There are numerous resources on this, but Aaron Lun describes why the original log-normalized values should be used for DE and visualizations of expression quite well here: > >For gene-based procedures like differential expression (DE) analyses or gene network construction, it is desirable to use the original log-expression values or counts. The corrected values are only used to obtain cell-level results such as clusters or trajectories. Batch effects are handled explicitly using blocking terms or via a meta-analysis across batches. We do not use the corrected values directly in gene-based analyses, for various reasons: > >It is usually inappropriate to perform DE analyses on batch-corrected values, due to the failure to model the uncertainty of the correction. This usually results in loss of type I error control, i.e., more false positives than expected. > >The correction does not preserve the mean-variance relationship. Applications of common DE methods like edgeR or limma are unlikely to be valid. > >Batch correction may (correctly) remove biological differences between batches in the course of mapping all cells onto a common coordinate system. Returning to the uncorrected expression values provides an opportunity for detecting such differences if they are of interest. Conversely, if the batch correction made a mistake, the use of the uncorrected expression values provides an important sanity check. > >In addition, the normalized values in SCT and integrated assays don’t necessary correspond to per-gene expression values anyway, rather containing residuals (in the case of the scale.data slot for each).
########IS SCTRANSFORM MORE ROBUST FOR DIFFERENT SEQUENCING DEPTHS??#########
Load 4 populations into single seurat object
SET SEED?????!!!!!
Set global variables
projectName <- "msAggr_sct"
jackstraw.dim <- 40source("msAggr_AnalysisCode/read_10XGenomics_data.R")
setwd("../cellRanger/") # temporarily changing wd only works if you run the entire chunk at onceWarning: The working directory was changed to /Users/heustonef/Desktop/10XGenomicsData/cellRanger inside a notebook chunk. The working directory will be reset when the chunk is finished running. Use the knitr root.dir option in the setup chunk to change the working directory for notebook chunks.data_file.list <- read_10XGenomics_data(sample.list = c("LSKm2", "CMPm2", "MEPm", "GMPm"))
object.data <-Read10X(data_file.list)seurat.object<- CreateSeuratObject(counts = object.data, min.cells = 3, min.genes = 200, project = projectName)Clean up to free memory
remove(object.data)Add mitochondrial metadata and plot some basic features
seurat.object[["percent.mt"]] <- PercentageFeatureSet(seurat.object, pattern = "^mt-")
VlnPlot(seurat.object, features = c("nFeature_RNA", "nCount_RNA", "percent.mt"), ncol = 3, pt.size = 0, fill.by = 'orig.ident', )
plot1 <- FeatureScatter(seurat.object, feature1 = "nCount_RNA", feature2 = "percent.mt", group.by = "orig.ident", pt.size = 0.01)
plot2 <- FeatureScatter(seurat.object, feature1 = "nCount_RNA", feature2 = "nFeature_RNA", group.by = "orig.ident", pt.size = 0.01)
plot1 + plot2
remove low quality cells ** require: nFeature_RNA between 200 and 4000 (inclusive) –require: percent.mt <5–???
** In previous run it’s possible that we’ll need to limit percent.mt because of MEPm weirdness. Will have to see if the additional 2000 MEPm cells found by cellranger v6
print(paste("original object:", nrow(seurat.object@meta.data), "cells", sep = " "))[1] "original object: 41950 cells"seurat.object <- subset(seurat.object,
subset = nFeature_RNA >=200 &
nFeature_RNA <= 4000
)
print(paste("new object:", nrow(seurat.object@meta.data), "cells", sep = " "))[1] "new object: 40815 cells"seurat.object <- PercentageFeatureSet(seurat.object, pattern = '^mt-', col.name = "percent.mt")Save base object
saveRDS(seurat.object, file = "msAggr_rawSeurat.rds")SCTransform
As previously mentioned, SCTransform() is best for batch correction, whereas NormalizeData(), ScaleData(), and FindVariableFeatures() are best for DGE. I’m struggling to make stable clusters, so I’m jumping ahead to SCTransform().
It does appear that SCTransform() data are stored in the SCT assay slot, which does not overwrite other normalization slots. It will become the default slot once populated.
seurat.object <- SCTransform(seurat.object,
method = "glmGamPoi",
vars.to.regress = c("nCount_RNA", "nFeature_RNA")
) # method = "glmGamPoi" speeds processTada! Now lets do some plotting and clustering!
linear dimensional reduction. Default are based on VariableFeatures, but can be changed
seurat.object <- RunPCA(seurat.object, features = VariableFeatures(object = seurat.object), verbose = FALSE)VizDimLoadings(seurat.object, dims = 1:6, nfeatures = 10, reduction = "pca", ncol = 2)DimPlot colored by orig.ident
DimPlot(seurat.object, reduction = "pca", group.by = "orig.ident")source("msAggr_AnalysisCode/PercentVariance.R")ElbowPlot(seurat.object, ndims = 50)
percent.variance(seurat.object@reductions$pca@stdev)Variance described by dimensionality
Number of PCs describing X% of variance
tot.var <- percent.variance(seurat.object@reductions$pca@stdev, plot.var = FALSE, return.val = TRUE)
paste0("Num pcs for 80% variance:", length(which(cumsum(tot.var) <= 80)))
paste0("Num pcs for 85% variance:", length(which(cumsum(tot.var) <= 85)))
paste0("Num pcs for 90% variance:", length(which(cumsum(tot.var) <= 90)))
paste0("Num pcs for 95% variance:", length(which(cumsum(tot.var) <= 95)))
Dim33
Cover 95% variability
ndim <- 33seurat.object <- RunUMAP(seurat.object, dims = 1:ndim)
seurat.object <- FindNeighbors(seurat.object, dims = 1:ndim)
for(x in c(0.5, 1, 1.5, 2, 2.5)){
seurat.object <- FindClusters(seurat.object, resolution = x)
}Plotting…
for (meta.col in colnames(seurat.object@meta.data)){
if(grepl(pattern = ("SCT_snn_res"), x = meta.col)==TRUE){
myplot <- DimPlot(seurat.object,
group.by = meta.col,
reduction = "umap",
cols = colorRamps::primary.colors(n = length(levels(seurat.object@meta.data[[meta.col]])))
) +
ggtitle(paste0("msAggr sct dim31 res", gsub("SCT_snn_res", "", meta.col) ))
plot(myplot)
}
}saveRDS(seurat.object, file = "msAggr_sctdim33.rds")Evaluate cluster stability
Must ensure we have the right cluster stability, that is, cells that start in the same cluster tend to stay in the same cluster. If your data is over-clustered, cells will bounce between groups.
Following [this tutorial by Matt O.].https://towardsdatascience.com/10-tips-for-choosing-the-optimal-number-of-clusters-277e93d72d92.
Clustree
Previously my favourite has been Clustree, which gives a nice visual NB: For some reason clustree::clustree() didn’t work, whereas library(clustree) followed by clustree() did.
clustree(seurat.object, prefix = "SCT_snn_res.", node_colour = "sc3_stability") +
scale_color_continuous(low = 'red3', high = 'white')Basically no stability difference compared to before.
Dim22
Cover 90% variability
ndim <- 22seurat.object <- RunUMAP(seurat.object, dims = 1:ndim)
seurat.object <- FindNeighbors(seurat.object, dims = 1:ndim)
for(x in c(0.5, 1, 1.5, 2, 2.5)){
seurat.object <- FindClusters(seurat.object, resolution = x)
}Plotting…
DimPlot(seurat.object, group.by = "orig.ident", reduction = "umap")DimPlot(seurat.object, group.by = "orig.ident", reduction = "umap")
for (meta.col in colnames(seurat.object@meta.data)){
if(grepl(pattern = ("SCT_snn_res"), x = meta.col)==TRUE){
myplot <- DimPlot(seurat.object,
group.by = meta.col,
reduction = "umap",
cols = colorRamps::primary.colors(n = length(levels(seurat.object@meta.data[[meta.col]])))
) +
ggtitle(paste0("msAggr sct dim", ndim, "res", gsub("SCT_snn_res", "", meta.col) ))
plot(myplot)
}
}
fav.genes <- c("Gata1", "Gata2", "Pf4", "Dntt", "Mpo", "Meis1", "Irf8", "Elane", "Fli1", "Zfpm1", "proc")FeaturePlot(seurat.object, features = fav.genes, ncol = 3)Warning in FetchData(object = object, vars = c(dims, "ident", features), :
The following requested variables were not found: proc
Evaluate cluster stability
Must ensure we have the right cluster stability, that is, cells that start in the same cluster tend to stay in the same cluster. If your data is over-clustered, cells will bounce between groups.
Following [this tutorial by Matt O.].https://towardsdatascience.com/10-tips-for-choosing-the-optimal-number-of-clusters-277e93d72d92.
Clustree
Previously my favourite has been Clustree, which gives a nice visual NB: For some reason clustree::clustree() didn’t work, whereas library(clustree) followed by clustree() did.
clustree(seurat.object, prefix = "SCT_snn_res.", node_colour = "sc3_stability") +
scale_color_continuous(low = 'red3', high = 'white')Hard to tell if dim33 or dim22 is better, but if feels like dim22 has less cross-over between res0.5 and res1.0. Will continue at dim22, and see how things change between res0.5 and res1.0
clustree(seurat.object, prefix = "SCT_snn_res.", node_colour = "sc3_stability") +
scale_color_continuous(low = 'red3', high = 'white')
MitoC in
# Number of filtered cells left in each pop
sapply(c("LSKm2", "CMPm2", "MEPm", "GMPm"), function(x) (c(nrow(seurat.object@meta.data[seurat.object@meta.data$orig.ident == x,]))))LSKm2 CMPm2 MEPm GMPm
11004 12342 8791 8678 Why are clusters so unstable?
Differences could include: * cells in each population (cellranger v6 includes more cells than cellranger v1, especially in MEP) * –dimensionality is incorrect– * ScaleData didnt account for regression factors (e.g., “nCounts_RNA” or “nFeatures_RNA”) * Incorrect normalization/scaling method * –Clustering is too strict or not strict enough– * neighborhood analysis used wrong parameters * Should include mitoC filter (there’s a chunk of MEP w/ mitoC @ ~40%) * –SCTransform accounts better for sources of variability–
Regress out cell cycle
One thing I’ve probably done in the past is regress out cell cycle factors. Did not do that here. Repeat analysis but include cell cycle regression.
Cell cycle scoring comes after RunPCA()
Step 1: load in raw object
for (x in c("LSKm2", "CMPm2", "MEPm", "GMPm")){
h = hist(seurat.object@meta.data[seurat.object@meta.data$orig.ident == x, 'percent.mt'], breaks = 30, plot = FALSE)
h$density = h$counts/sum(h$counts)*100
plot(h,freq=FALSE, main = paste(x, "percent mitoC"), xlab = "percent mitoC", ylab = "Frequency")
}
Since SCTransform() is best for different correcting different sequencing depths, going to stick with that
The Cell Cycle Regression Vignette says to do NormalizeData(), FindVariableFeatures(), ScaleData(), RunPCA(), CellCycleScoring(), then RunPCA() with s.genes and g2m.genes. It seems to be more complicated with SCTransform() because you don’t want to normalize normalized data, but you need normalized data to properly run CellCycleScoring(). Found discussion board and this is the answer that makes the most sense to me
seurat.object <- readRDS("msAggr_rawSeurat.rds")so let’s try this
seurat.object <- SCTransform(seurat.object, assay = 'RNA', new.assay.name = "SCT", method = "glmGamPoi", vars.to.regress = c("nFeature_RNA", "nCount_RNA"))Cell cycle scoring
seurat.object <- SCTransform(seurat.object, assay = 'RNA', new.assay.name = "SCT", method = "glmGamPoi", vars.to.regress = c("nFeature_RNA", "nCount_RNA"))Calculating cell attributes from input UMI matrix: log_umi
Variance stabilizing transformation of count matrix of size 17709 by 40815
Model formula is y ~ log_umi
Get Negative Binomial regression parameters per gene
Using 2000 genes, 5000 cells
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|=================================================================================================================================================================================================================| 100%Found 108 outliers - those will be ignored in fitting/regularization step
Second step: Get residuals using fitted parameters for 17709 genes
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Wall clock passed: Time difference of 2.586646 mins
Determine variable features
Place corrected count matrix in counts slot
Regressing out nFeature_RNA, nCount_RNA
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Centering data matrix
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|==================================================== | 25%
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|======================================================================================================== | 50%
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Set default assay to 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